An honest quant tutorial, no fluff.
A growing series covering factors, strategies, backtest pitfalls, risk, alpha decay, the field's history, and where AI actually helps. Each chapter ends with a concrete exercise you run in AlphaHub.
The chapters
How to read this tutorial
What the eight chapters cover, who they are written for, and how to use AlphaHub alongside as you go.
What is a quant factor?
From Fama-French to the modern style factor zoo. Value, momentum, quality, low-volatility — what each one measures and how to compute it in pandas.
What is a trading strategy?
Factor to signal to portfolio to execution to metrics. The full pipeline plus the four numbers — Sharpe, max drawdown, hit rate, turnover — that decide whether it lives.
Backtest pitfalls
Look-ahead bias, survivorship bias, in-sample tuning, transaction-cost models. The boring discipline that separates plausible returns from real ones.
Risk management
Drawdown anatomy, Kelly versus volatility targeting, correlation regimes, and what 2020-03 / 2015-06 teach about the tails.
Alpha decay
Why every working strategy stops working. How to monitor IC decay, crowding, and regime breaks before they show up in PnL.
A short history of quant
Ed Thorp, D.E. Shaw, Renaissance, LTCM, the 2007 quant meltdown, and what each generation learned about edge.
AI in quant
Where machine learning earns its keep, where it overfits, and how language models are starting to change research workflow.
Template-backed walkthroughs
Each article ships with a fork-ready strategy template you can drop into your workspace. Filter by market, style, or difficulty.
How to read a backtest report without fooling yourself
A checklist for evaluating any strategy you did not write yourself. Required reading before forking.
Momentum: the only factor that keeps working
From Fama–French critique to A-share rotation — a working primer on the most robust anomaly in finance.
What is mean reversion, and when does it stop working?
From the textbook z-score to gamma-driven intraday fades, a tour of when price snaps back — and when it does not.
Deep RL · Perp Funding Farmer
A PPO agent trained to harvest funding carry across 40 crypto perps with dynamic directional hedge. Advanced ML with a real reward signal.
SPY GEX Fade · trading options microstructure
An intraday fade strategy built on dealer gamma exposure, executed with a 3-minute trigger. Teaches why positioning data beats price data.
Mean Reversion on S&P 500 · the entry-level quant strategy
Short-term mean reversion with a z-score signal and a breadth-based regime gate. The cleanest first strategy for learning portfolio construction.
MA-Cross on HSTECH · regime-aware trend following
A disciplined 5/20 moving-average crossover on Hang Seng TECH, filtered by realised volatility. Designed to teach when trend-following works and when it quietly bleeds.
BTC/ETH Pair Trade · stat-arb with a Kalman filter
Classic cointegration trading on the crypto majors, with a dynamic hedge ratio and funding-aware position sizing. Demonstrates why static OLS hedging fails.
LightGBM Factor Stack · CSI 300
Gradient-boosted nonlinear factor interactions on A-shares, with proper walk-forward validation and turnover caps. A working production ML template.
50ETF Momentum Rotation · the most robust factor
Monthly cross-sectional momentum on CN blue chips with an Amihud illiquidity penalty. The simplest viable production momentum strategy.
Dividend Aristocrats Plus · a defensive quality sleeve
A low-turnover basket of S&P 500 companies with 25+ years of dividend growth, filtered by payout ratio and leverage. The ideal first systematic allocation.
USD/CNY Carry · when a factor ages
A classic FX carry trade, now gated by PBOC fixing behaviour. A case study in what happens when central banks decide a factor is inconvenient.